Word Spotting: A New Approach to Indexing Handwriting
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Keyword-guided word spotting in historical printed documents using synthetic data and user feedback
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ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
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Handwritten-Word Spotting Using Biologically Inspired Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards an omnilingual word retrieval system for ancient manuscripts
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Handwritten word-spotting using hidden Markov models and universal vocabularies
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Handwritten Word Image Retrieval with Synthesized Typed Queries
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
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ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Generation of learning samples for historical handwriting recognition using image degradation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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We propose a method to perform text searches on handwritten word image databases when no ground-truth data is available to learn models or select example queries. The approach proceeds by synthesizing multiple images of the query string using different computer fonts. While this idea has been successfully applied to printed documents in the past, its application to the handwritten domain is not straightforward. Indeed, the domain mismatch between queries (synthetic) and database images (handwritten) leads to poor accuracy. Our solution is to represent the queries with robust features and use a model that explicitly accounts for the domain mismatch. While the model is trained using synthetic images, its generative process produces samples according to the distribution of handwritten features. Furthermore, we propose an unsupervised method to perform font selection which has a significant impact on accuracy. Font selection is formulated as finding an optimal weighted mixture of fonts that best approximates the distribution of handwritten low-level features. Experiments demonstrate that the proposed method is an effective way to perform queries without using any human annotated example in any part of the process.